below marketmost similar roles pay hereabove market
This role pays more than
68%
of similar roles. Most pay
$202,187–$254,750
— the blue band above.
At the midpoint, this role pays about
$251k
versus about
$228k
for comparable roles.
Based on 240 similar postings.
Employer
About DoorDash, Inc
DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States.
DoorDash, Inc currently has
97 open roles
on FindRole.
Listed pay typically runs
$163,800–$240,900
across 96 roles with salary data.
The Staff Machine Learning Scientist, Applied Causal Inference joins a senior pod of causal ML and econometrics experts to build the causal spine for a large-scale consumer marketplace. This role focuses on developing causal ML systems for New Verticals, including grocery, retail, and alcohol. You will design and productionize uplift and heterogeneous treatment effect models for consumer lifecycle value, retention, and promotions. Key responsibilities include building counterfactual evaluation frameworks for ranking, search, and marketplace interventions, as well as designing surrogate metrics. You will utilize advanced methodologies such as doubly robust estimation, double ML, IV, diff-in-diff, CUPED, contextual bandits, and off-policy evaluation. The position requires strong ML engineering skills to build reliable pipelines and translate complex causal models into production systems that shape ranking, targeting, and budget allocation.
Design, build, and productionize causal ML systems to influence marketplace decisions across New Verticals.
Build uplift and heterogeneous treatment effect models for consumer lifecycle value, promotions, and retention.
Develop counterfactual evaluation frameworks for ranking, recommendations, search, and marketplace interventions.
Create systems connecting experimentation, observational data, and ML decisioning to handle noisy or incomplete data.
Design surrogate metrics and early indicators to help teams move faster while preserving marketplace health.
Translate causal models into production systems for ranking, targeting, budget allocation, and inventory-aware discovery.
Partner with econometrics leaders to apply methods like doubly robust estimation, IV, and double ML.
Debug causal claims and provide guidance on when to trust model outputs across ML teams.
What we're looking for
Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
Experience shipping models or decision systems in production, ideally in high-scale consumer marketplaces.
Strong ML engineering ability to build reliable pipelines, train models, and evaluate them rigorously.
Proficiency in methods such as doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation.
Ability to design and productionize causal ML systems like uplift models, surrogate metrics, and counterfactual evaluation frameworks.
Strong product judgment to connect causal methods to business decisions rather than just optimizing offline metrics.
Ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.